724 Background: RMC-9805 is a potent, oral, RAS(ON) G12D-selective, covalent, tri-complex inhibitor targeting the active, GTP-bound state of oncogenic RAS G12D isoforms. Despite low response rates to current standard of care (SOC), no RAS-targeted therapy is approved for G12D PDAC. Methods: In this Phase 1 study (NCT06040541), patients with previously treated, advanced KRAS G12D solid tumors received escalating RMC-9805 doses (150-1200 mg once daily [QD] or 300-600 mg twice daily [BID]). Treatment cycles were every 3 wks. Antitumor activity was assessed every 6 wks the first 24 wks then every 9 wks. Additional patients were enrolled at RMC-9805 doses that cleared the dose-limiting toxicity (DLT) evaluation to further characterize pharmacokinetics, safety, antitumor activity, and biomarkers. Plasma for ctDNA analysis was collected at baseline (BL; cycle 1, day 1 [C1D1] or screening), and on treatment (OT; C2D1 or C3D1). Results: As of Sept 2, 2024, 179 patients with KRAS G12D solid tumors (most having PDAC; n=104 [58%]) received 5 escalating dose levels of RMC-9805 (150-1200 mg daily). No DLTs or Grade 4 or 5 treatment-related adverse events (TRAEs) were reported, and the maximum tolerated dose (MTD) was not reached. Among patients who received a candidate recommended phase 2 dose (RP2D) of 1200 mg daily (1200 mg QD [n=60] or 600 mg BID [n=39]), the most common (≥10% of patients) TRAEs were nausea (27%), diarrhea (20%), vomiting (15%), and rash (10%), all of which were Grade 1 or 2 severity. One Grade 3 TRAE (ALT elevation) was observed in a patient with PDAC and a history of ALT elevation, biliary stenting, and liver metastasis. No patients treated with 1200 mg daily discontinued treatment due to TRAEs and 4% of patients had dose reductions due to TRAEs. In patients with PDAC receiving RMC-9805 daily at 1200 mg QD (n=20) or 600 mg BID (n=20) who enrolled at least 14 wks prior to data cutoff, the objective response rate (confirmed response or pending confirmation) was 30%, and the disease control rate was 80%. Of 28 patients who had KRAS G12D mutations detected in ctDNA at BL and were evaluable for OT assessment, 86% had an OT decrease >50% from BL of detectable KRAS G12D mutations, and 39% had a 100% OT clearance. Updated data will be presented. Conclusions: Oral RMC-9805 showed encouraging initial antitumor activity with early and deep reductions in KRAS G12D ctDNA in patients with KRAS G12D PDAC. Tolerability was favorable relative to SOC chemotherapy for PDAC and manageable. This overall safety profile and antitumor activity support continued evaluation as monotherapy in patients with KRAS G12D PDAC, and in combination with chemotherapy and targeted therapies, including the RAS(ON) multi-selective inhibitor RMC-6236. Clinical trial information: NCT06040541 .

Introduction: Sepsis has the characteristics of high incidence, high mortality of ICU patients. Early assessment of disease severity and risk stratification of death in patients with sepsis, and further targeted intervention are very important. The purpose of this study was to develop machine learning models based on sequential organ failure assessment (SOFA) components to early predict in-hospital mortality in ICU patients with sepsis and evaluate model performance. Methods: Patients admitted to ICU with sepsis diagnosis were extracted from MIMIC-IV database for retrospective analysis, and were randomly divided into training set and test set in accordance with 2:1. Six variables were included in this study, all of which were from the scores of 6 organ systems in SOFA score. The machine learning model was trained in the training set and evaluated in the validation set. Six machine learning methods including linear regression analysis, least absolute shrinkage and selection operator (LASSO), Logistic regression analysis (LR), Gaussian Naive Bayes (GNB) and support vector machines (SVM) were used to construct the death risk prediction models, and the accuracy, area under the receiver operating characteristic curve (AUROC), Decision Curve Analysis (DCA) and K-fold cross-validation were used to evaluate the prediction performance of developed models. Result: A total of 23,889 patients with sepsis were enrolled, of whom 3659 died in hospital. Three feature variables including renal system score, central nervous system score and cardio vascular system score were used to establish prediction models. The accuracy of the LR, GNB, SVM were 0.851, 0.844 and 0.862, respectively, which were better than linear regression analysis (0.123) and LASSO (0.130). The AUROCs of LR, GNB and SVM were 0.76, 0.76 and 0.67, respectively. K-fold cross validation showed that the average AUROCs of LR, GNB and SVM were 0.757 ± 0.005, 0.762 ± 0.006, 0.630 ± 0.013, respectively. For the probability threshold of 5–50%, LY and GNB models both showed positive net benefits. Conclusion: The two machine learning-based models (LR and GNB models) based on SOFA components can be used to predict in-hospital mortality of septic patients admitted to ICU.

Aims: Ideal glycemic control is of great importance for diabetic patients during public health emergencies of infectious diseases as long-term hyperglycemic are not only associated with chronic complications but also vital drivers of common and life-threatening infections. The present study was designed to investigate the changes of blood glucose levels in elderly subjects with type 2 diabetes(T2D) during COVID-19 outbreak. Methods: This retrospective study focused on the T2D outpatients at Fujian Provincial Hospital aged 65 years old and above who received baseline test for fasting plasma glucose and/or glycated hemoglobin (HbA1c) between January 1, 2019 and March 8, 2019 and were followed up on fasting plasma glucose and/or HbA1c in the same period in 2020. The baseline and follow-up data were analyzed with the paired-samples T-test. Results: A total of 135 elderly subjects with T2D with baseline and follow-up fasting plasma glucose and 50 elderly subjects with T2D with baseline and follow-up HbA1c were analyzed, respectively. The baseline and follow-up fasting plasma glucose were 7.08 ± 1.80 and 7.48±2.14 mmol/L, respectively (P=0.008). The baseline and follow-up HbA1c were 7.2±1.7% and 7.4±1.8%, respectively (P=0.158). Conclusions: Elderly subjects with T2D had higher fasting plasma glucose levels during COVID-19 outbreak. We should pay more attension to the management of diabetics during public health emergencies.

To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients. This multicenter retrospective cohort study analyzed electronic medical records of sepsis patients using machine learning methods. We evaluated model performance in predicting sepsis outcomes within the first 24 h of ICU admission across US and Chinese healthcare settings. From 31 clinical features, machine learning models demonstrated significantly better predictive performance than traditional approaches for sepsis outcomes. While linear regression achieved low test scores (0.25), machine learning methods reached scores of 0.78 and AUCs above 0.8 in testing. Importantly, these models maintained robust performance (scores 0.63–0.77) in external validation. The application of machine learning-based prediction models for sepsis could significantly improve patient outcomes through early detection and timely intervention in the critical first 24 h of ICU admission, supporting clinical decision-making.

Background: The role of thioredoxin-interacting protein (TXNIP) in lipopolysaccharide-induced liver injury in mice has been reported, but the underlying mechanisms are poorly understood. Methods: We overexpressed deubiquitinase in cells overexpressing TXNIP and then detected the level of TXNIP to screen out the deubiquitinase regulating TXNIP; the interaction between TXNIP and deubiquitinase was verified by coimmunoprecipitation. After knockdown of a deubiquitinase and overexpression of TXNIP in Huh7 and HepG2 cells, lipopolysaccharide was used to establish a cellular inflammatory model to explore the role of deubiquitinase and TXNIP in hepatocyte inflammation. Results: In this study, we discovered that ubiquitin-specific protease 5 (USP5) interacts with TXNIP and stabilizes it through deubiquitylation in Huh-7 and HepG2 cells after treatment with lipopolysaccharide. In lipopolysaccharide-treated Huh-7 and HepG2 cells, USP5 knockdown increased cell viability, reduced apoptosis, and decreased the expression of inflammatory factors, including NLRP3, IL-1β, IL-18, ASC, and procaspase-1. Overexpression of TXNIP reversed the phenotype induced by knockdown USP5. Conclusions: In summary, USP5 promotes lipopolysaccharide-induced apoptosis and inflammatory response by stabilizing the TXNIP protein. Export